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Multivariate time series anomaly detection via dynamic graph attention network and Informer

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Abstract

In the industrial Internet, industrial software plays a central role in enhancing the level of intelligent manufacturing. It enables the promotion of digital collaborative services. Effective anomaly detection of multivariate time series can ensure the quality of industrial software. Extensive research has been conducted on time series anomaly detection to identify abnormal data. However, detecting anomalies in multivariate time series, which consist of high-dimensional, high-noise, and random data, poses significant challenges. The states of different timestamps within a time series sample can influence the overall correlation of sensor features. Unfortunately, existing methods often overlook this impact, making it difficult to capture subtle variations in the delayed response of attacked sensors.Consequently, there are false alarms and abnormal omissions. To address these limitations, this paper proposes an anomaly detection method called DGINet. DGINet leverages a dynamic graph attention network and Informer to capture and integrate feature correlation across different time states. By combining GRU and Informer, DGINet effectively captures continuous correlations in long time series. Moreover, DGINet simultaneously optimizes the reconstruction and forecasting modules, enhancing its overall performance. Experimental results on four benchmark datasets demonstrate that DGINet outperforms state-of-the-art methods by achieving up to a 2\(\%\) improvement in accuracy. Further analysis reveals that DGINet excels in accurately detecting anomalies in long time series and locating candidate abnormal attack points.

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The text provides the source of the relevant data, and additional data will be made available on request.

Notes

  1. https://github.com/khundman/telemanom

  2. https://github.com/khundman/telemanom

  3. https://itrust.sutd.edu.sg/itrustlabs_datasets/dataset_info

  4. https://github.com/NetManAIOps/OmniAnomaly

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62162003) and Guangxi University Natural Science and Technology innovation and development doubling plan project(NO. 2023BZXM002)

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Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, Nanning, 530004, Guangxi, China

    Xiangheng Huang, Ningjiang Chen, Ziyue Deng & Suqun Huang

  2. The Guangxi Intelligent Digital Services Research Center of Engineering Technology, Nanning, 530004, Guangxi, China

    Ningjiang Chen

  3. Key Laboratory of Parallel, Distributed and Intelligent Computing, Education Department of Guangxi Zhuang Autonomous Region, Nanning, 530004, Guangxi, China

    Ningjiang Chen

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  1. Xiangheng Huang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiangheng Huang, Ningjiang Chen, Ziyue Deng and Suqun Huang. The first draft of the manuscript was written by Xiangheng Huang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ningjiang Chen.

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Huang, X., Chen, N., Deng, Z. et al. Multivariate time series anomaly detection via dynamic graph attention network and Informer. Appl Intell 54, 7636–7658 (2024). https://doi.org/10.1007/s10489-024-05575-y

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